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-if(getRversion() >= '2.15.1') utils::globalVariables("ds")
-
-#' Plot a fitted nonlinear mixed model obtained via an mmkin row object
-#'
-#' @param x An object of class \code{\link{nlme.mmkin}}
-#' @param i A numeric index to select datasets for which to plot the nlme fit,
-#' in case plots get too large
-#' @inheritParams plot.mkinfit
-#' @param standardized Should the residuals be standardized? Only takes effect if
-#' `resplot = "time"`.
-#' @param rel.height.legend The relative height of the legend shown on top
-#' @param rel.height.bottom The relative height of the bottom plot row
-#' @param ymax Vector of maximum y axis values
-#' @param ncol.legend Number of columns to use in the legend
-#' @param nrow.legend Number of rows to use in the legend
-#' @param resplot Should the residuals plotted against time or against
-#' predicted values?
-#' @param col_ds Colors used for plotting the observed data and the
-#' corresponding model prediction lines for the different datasets.
-#' @param pch_ds Symbols to be used for plotting the data.
-#' @param lty_ds Line types to be used for the model predictions.
-#' @importFrom stats coefficients
-#' @return The function is called for its side effect.
-#' @author Johannes Ranke
-#' @examples
-#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
-#' function(x) x$data[c("name", "time", "value")])
-#' names(ds) <- paste0("ds ", 6:10)
-#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-#' A1 = mkinsub("SFO"), quiet = TRUE)
-#' \dontrun{
-#' f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
-#' plot(f[, 3:4], standardized = TRUE)
-#'
-#' library(nlme)
-#' # For this fit we need to increase pnlsMaxiter, and we increase the
-#' # tolerance in order to speed up the fit for this example evaluation
-#' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
-#' plot(f_nlme)
-#' }
-#' @export
-plot.nlme.mmkin <- function(x, i = 1:ncol(x$mmkin),
- obs_vars = names(x$mkinmod$map),
- standardized = TRUE,
- xlab = "Time",
- xlim = range(x$data$time),
- resplot = c("predicted", "time"),
- ymax = "auto", maxabs = "auto",
- ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),
- nrow.legend = ceiling((length(i) + 1) / ncol.legend),
- rel.height.legend = 0.03 + 0.08 * nrow.legend,
- rel.height.bottom = 1.1,
- pch_ds = 1:length(i),
- col_ds = pch_ds + 1,
- lty_ds = col_ds,
- frame = TRUE)
-{
-
- oldpar <- par(no.readonly = TRUE)
-
- fit_1 = x$mmkin[[1]]
- ds_names <- colnames(x$mmkin)
-
- degparms_optim <- coefficients(x)
- degparms_optim_names <- names(degparms_optim)
- degparms_fixed <- fit_1$fixed$value
- names(degparms_fixed) <- rownames(fit_1$fixed)
- degparms_all <- cbind(as.matrix(degparms_optim),
- matrix(rep(degparms_fixed, nrow(degparms_optim)),
- ncol = length(degparms_fixed),
- nrow = nrow(degparms_optim), byrow = TRUE))
- degparms_all_names <- c(degparms_optim_names, names(degparms_fixed))
- colnames(degparms_all) <- degparms_all_names
-
- odeini_names <- grep("_0$", degparms_all_names, value = TRUE)
- odeparms_names <- setdiff(degparms_all_names, odeini_names)
-
- residual_type = ifelse(standardized, "pearson", "response")
-
- observed <- cbind(x$data,
- residual = residuals(x, type = residual_type))
-
- n_plot_rows = length(obs_vars)
- n_plots = n_plot_rows * 2
-
- # Set relative plot heights, so the first plot row is the norm
- rel.heights <- if (n_plot_rows > 1) {
- c(rel.height.legend, c(rep(1, n_plot_rows - 1), rel.height.bottom))
- } else {
- c(rel.height.legend, 1)
- }
-
- layout_matrix = matrix(c(1, 1, 2:(n_plots + 1)),
- n_plot_rows + 1, 2, byrow = TRUE)
- layout(layout_matrix, heights = rel.heights)
-
- par(mar = c(0.1, 2.1, 0.6, 2.1))
-
- plot(0, type = "n", axes = FALSE, ann = FALSE)
- legend("center", bty = "n", ncol = ncol.legend,
- legend = c("Population", ds_names[i]),
- lty = c(1, lty_ds), lwd = c(2, rep(1, length(i))),
- col = c(1, col_ds),
- pch = c(NA, pch_ds))
-
-
- solution_type = fit_1$solution_type
-
- outtimes <- sort(unique(c(x$data$time,
- seq(xlim[1], xlim[2], length.out = 50))))
-
- pred_ds <- purrr::map_dfr(i, function(ds_i) {
- odeparms_trans <- degparms_all[ds_i, odeparms_names]
- names(odeparms_trans) <- odeparms_names # needed if only one odeparm
- odeparms <- backtransform_odeparms(odeparms_trans,
- x$mkinmod,
- transform_rates = fit_1$transform_rates,
- transform_fractions = fit_1$transform_fractions)
-
- odeini <- degparms_all[ds_i, odeini_names]
- names(odeini) <- gsub("_0", "", odeini_names)
-
- out <- mkinpredict(x$mkinmod, odeparms, odeini,
- outtimes, solution_type = solution_type,
- atol = fit_1$atol, rtol = fit_1$rtol)
- return(cbind(as.data.frame(out), ds = ds_names[ds_i]))
- })
-
- degparms_all_pop <- c(fixef(x), degparms_fixed)
-
- odeparms_pop_trans <- degparms_all_pop[odeparms_names]
- odeparms_pop <- backtransform_odeparms(odeparms_pop_trans,
- x$mkinmod,
- transform_rates = fit_1$transform_rates,
- transform_fractions = fit_1$transform_fractions)
-
- odeini_pop <- degparms_all_pop[odeini_names]
- names(odeini_pop) <- gsub("_0", "", odeini_names)
-
- pred_pop <- as.data.frame(
- mkinpredict(x$mkinmod, odeparms_pop, odeini_pop,
- outtimes, solution_type = solution_type,
- atol = fit_1$atol, rtol = fit_1$rtol))
-
- resplot <- match.arg(resplot)
-
- # Loop plot rows
- for (plot_row in 1:n_plot_rows) {
-
- obs_var <- obs_vars[plot_row]
- observed_row <- subset(observed, name == obs_var)
-
- # Set ylim to sensible default, or use ymax
- if (identical(ymax, "auto")) {
- ylim_row = c(0,
- max(c(observed_row$value, pred_ds[[obs_var]]), na.rm = TRUE))
- } else {
- ylim_row = c(0, ymax[plot_row])
- }
-
- # Margins for bottom row of plots when we have more than one row
- # This is the only row that needs to show the x axis legend
- if (plot_row == n_plot_rows) {
- par(mar = c(5.1, 4.1, 2.1, 2.1))
- } else {
- par(mar = c(3.0, 4.1, 2.1, 2.1))
- }
-
- plot(pred_pop$time, pred_pop[[obs_var]],
- type = "l", lwd = 2,
- xlim = xlim, ylim = ylim_row,
- xlab = xlab, ylab = obs_var, frame = frame)
-
- for (ds_i in seq_along(i)) {
- points(subset(observed_row, ds == ds_names[ds_i], c("time", "value")),
- col = col_ds[ds_i], pch = pch_ds[ds_i])
- lines(subset(pred_ds, ds == ds_names[ds_i], c("time", obs_var)),
- col = col_ds[ds_i], lty = lty_ds[ds_i])
- }
-
- if (identical(maxabs, "auto")) {
- maxabs = max(abs(observed_row$residual), na.rm = TRUE)
- }
-
- if (identical(resplot, "time")) {
- plot(0, type = "n", xlim = xlim, xlab = "Time",
- ylim = c(-1.2 * maxabs, 1.2 * maxabs),
- ylab = if (standardized) "Standardized residual" else "Residual")
-
- abline(h = 0, lty = 2)
-
- for (ds_i in seq_along(i)) {
- points(subset(observed_row, ds == ds_names[ds_i], c("time", "residual")),
- col = col_ds[ds_i], pch = pch_ds[ds_i])
- }
- }
-
- if (identical(resplot, "predicted")) {
- plot(0, type = "n",
- xlim = c(0, max(pred_ds[[obs_var]])),
- xlab = "Predicted",
- ylim = c(-1.2 * maxabs, 1.2 * maxabs),
- ylab = if (standardized) "Standardized residual" else "Residual")
-
- abline(h = 0, lty = 2)
-
- for (ds_i in seq_along(i)) {
- observed_row_ds <- merge(
- subset(observed_row, ds == ds_names[ds_i], c("time", "residual")),
- subset(pred_ds, ds == ds_names[ds_i], c("time", obs_var)))
- points(observed_row_ds[c(3, 2)],
- col = col_ds[ds_i], pch = pch_ds[ds_i])
- }
- }
- }
-}

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